diff --git a/OAI/types/common.py b/OAI/types/common.py
index 065ec5d..ca636b9 100644
--- a/OAI/types/common.py
+++ b/OAI/types/common.py
@@ -21,7 +21,6 @@ class CommonCompletionRequest(BaseModel):
     # Extra OAI request stuff
     best_of: Optional[int] = Field(description = "Not parsed. Only used for OAI compliance.", default = None)
     echo: Optional[bool] = Field(description = "Not parsed. Only used for OAI compliance.", default = False)
-    logit_bias: Optional[Dict[str, float]] = Field(description = "Not parsed. Only used for OAI compliance.", default = None)
     logprobs: Optional[int] = Field(description = "Not parsed. Only used for OAI compliance.", default = None)
     n: Optional[int] = Field(description = "Not parsed. Only used for OAI compliance.", default = 1)
     suffix: Optional[str] = Field(description = "Not parsed. Only used for OAI compliance.", default = None)
@@ -55,6 +54,7 @@ class CommonCompletionRequest(BaseModel):
     mirostat_eta: Optional[float] = 0.1
     add_bos_token: Optional[bool] = True
     ban_eos_token: Optional[bool] = False
+    logit_bias: Optional[Dict[int, float]] = None
 
     # Aliased variables
     repetition_range: Optional[int] = Field(
@@ -78,6 +78,7 @@ class CommonCompletionRequest(BaseModel):
             "add_bos_token": self.add_bos_token,
             "ban_eos_token": self.ban_eos_token,
             "token_healing": self.token_healing,
+            "logit_bias": self.logit_bias,
             "temperature": self.temperature,
             "temperature_last": self.temperature_last,
             "top_k": self.top_k,
diff --git a/model.py b/model.py
index ff418ee..5c8be3d 100644
--- a/model.py
+++ b/model.py
@@ -345,6 +345,7 @@ class ModelContainer:
                 'max_tokens' (int): Max no. tokens in response (default: 150)
                 'add_bos_token' (bool): Adds the BOS token to the start of the prompt (default: True)
                 'ban_eos_token' (bool): Bans the EOS token from generation (default: False)
+                'logit_bias' (Dict[int, float]): Biases specific tokens to either show up more or less (default: None)
                 'stream_interval' (float): Interval in seconds between each output chunk (default: immediate)
                 'generate_window' (int): Space to reserve at the end of the model's context when generating.
                     Rolls context window by the same amount if context length is exceeded to allow generating past
@@ -396,7 +397,9 @@ class ModelContainer:
         gen_settings.token_repetition_decay = coalesce(kwargs.get("repetition_decay"), fallback_decay, 0)
 
         stop_conditions: List[Union[str, int]] = unwrap(kwargs.get("stop"), [])
+        add_bos_token = unwrap(kwargs.get("add_bos_token"), True)
         ban_eos_token = unwrap(kwargs.get("ban_eos_token"), False)
+        logit_bias = kwargs.get("logit_bias")
 
         # Override sampler settings for temp = 0
         if gen_settings.temperature == 0:
@@ -406,16 +409,31 @@ class ModelContainer:
             gen_settings.typical = 0
 
         # Log generation options to console
+        # Some options are too large, so log the args instead
         log_generation_params(
+            max_tokens = max_tokens,
             **vars(gen_settings),
             token_healing = token_healing,
-            max_tokens = max_tokens,
-            stop_conditions = stop_conditions
+            add_bos_token = add_bos_token,
+            ban_eos_token = ban_eos_token,
+            stop_conditions = stop_conditions,
+            logit_bias = logit_bias
         )
 
         # Log prompt to console
         log_prompt(prompt)
 
+        # Set logit bias
+        if logit_bias:
+            # Create a vocab tensor if it doesn't exist for token biasing
+            if gen_settings.token_bias is None:
+                padding = -self.tokenizer.config.vocab_size % 32
+                gen_settings.token_bias = torch.zeros((self.tokenizer.config.vocab_size + padding,), dtype = torch.float)
+
+            # Map logits to the tensor with their biases
+            for token, bias in logit_bias.items():
+                gen_settings.token_bias[token] = bias
+
         # Ban the EOS token if specified. If not, append to stop conditions as well.
         # Set this below logging to avoid polluting the stop strings array
         if ban_eos_token:
@@ -429,7 +447,7 @@ class ModelContainer:
         # Tokenized context
         ids = self.tokenizer.encode(
             prompt,
-            add_bos = unwrap(kwargs.get("add_bos_token"), True),
+            add_bos = add_bos_token,
             encode_special_tokens = True
         )
         context_len = len(ids[0])